Toward Flexible & Resilient 100% Renewable Power Systems with Hybrid Energy Storage Systems

Abstract

The global transition to renewable energy systems is imperative for mitigating climate change, yet the inherent variability and unpredictability of renewable energy sources (RES) pose formidable challenges to grid stability. Simultaneously, escalating water scarcity demands energy-intensive desalination, while decarbonization mandates require displacing fossil fuels and integrating carbon capture technologies (CCT) with legacy thermal plants. Furthermore, hydrogen energy systems (HES) have emerged as a critical enabler for long-duration energy storage and sector coupling. Yet, their full potential remains untapped due to fragmented modelling and operational silos. Achieving a sustainable, resilient power grid necessitates harmonizing these interconnected systems (energy, water, and carbon) through co-optimized frameworks that leverage flexibility across demand response (DR), storage, and multi-carrier energy networks.

Existing research inadequately addresses the interconnections between energy, water, and emissions management. While reverse osmosis desalination (RO-WDP) is recognized for its energy intensity, its potential as a grid flexibility asset remains unexplored, particularly its temperature-dependent operational dynamics. Studies on carbon capture often isolate CCT from system-level optimization, overlooking its energy penalties and interplay with RES variability. Similarly, hydrogen systems are frequently modelled in isolation: electrolyzers, storage, and fuel cells are analyzed independently, neglecting logistical constraints in hydrogen transportation (e.g., route capacities and delivery schedules) and integrating centralized production with distributed demand. Traditional scenario-generation methods rely on static assumptions, failing to adapt to real-time uncertainties in RES and demand. At the same time, multi-objective frameworks prioritize cost over risk, limiting their applicability to real-world volatility. Prior works lack comprehensive coordination of these components, leading to suboptimal trade-offs between sustainability, reliability, and affordability.

This thesis is covered in four studies. The first study pioneers a stochastic co-optimization model integrating grid-connected RO-WDPs as dynamic DR assets. Unlike conventional DR models, it incorporates temperature-dependent power consumption profiles of RO-WDPs, enabling load-shifting and curtailment strategies that align desalination schedules with RES availability. A security-constrained unit commitment (SCUC) framework balances water demand reliability with operational cost minimization, resolving conflicts between energy and water sustainability. This study demonstrates that temperature-responsive RO-WDPs enhance grid flexibility by dynamically aligning desalination loads with RES surpluses, reducing peak demand stress and operational costs without compromising water security.

The second study introduces a risk-constrained, two-objective stochastic framework unifying CCT-equipped coal plants, RES, and hydropower. The model dynamically optimises carbon capture rates while coordinating DR programs by embedding a hidden Markov process (HMP) to forecast RES and demand uncertainties. An enhanced ε-constraint method generates Pareto-optimal solutions, reconciling cost and emission objectives without weighting biases. This study achieves substantial emission reductions by optimizing carbon capture cycles alongside RES and DR, proving that CCT integration need not compromise grid reliability or economic efficiency.

The third study advances a risk-aware multi-objective framework co-optimizing power-water-carbon interdependencies. It introduces temperature-responsive RO desalination as a dual-purpose asset, meeting water demand while providing grid flexibility via water storage buffers. Conditional value-at-risk (CVaR) quantifies operational risks, while an enhanced ε-constraint technique balances cost, emissions, and risk aversion, enabling robust decision-making under uncertainty. This study validates that co-optimizing water storage and DR strategies mitigate renewable intermittency, flattening electricity prices and enhancing grid resilience against supply-demand mismatches.

The fourth study devises a unified HES framework integrating hydrogen production (alkaline electrolyzers), liquid organic hydrogen carrier (LOHC)-based transportation, and fuel cells. Novel linearization techniques simplify nonlinear electrolyzer and fuel cell dynamics, enabling computationally efficient, large-scale optimization. A two-stage stochastic model, enhanced by HMP-based scenario generation, coordinates hydrogen production schedules with RES curtailment mitigation and truck routing logistics, bridging gaps between centralized infrastructure and distributed demand. This study reveals that coordinated HES operations minimize RES curtailment, stabilize grid frequency through hydrogen-to-power conversion, and streamline hydrogen logistics via optimized truck routing and storage.

These frameworks redefine modern power systems by fostering deep interconnectivity across energy, water, and carbon domains. This work transforms standalone infrastructures into adaptive, multi-functional networks by integrating reverse osmosis desalination plants, carbon capture retrofits, hydrogen energy systems, and demand response programs. Such integration enhances grid stability by dynamically balancing supply-demand mismatches while maximizing renewable energy utilization, turning intermittency into opportunity. Coordinated operation aligns energy-intensive processes like desalination and hydrogen production with renewable generation peaks, while carbon capture mitigates residual emissions from fossil assets. This thesis bridges critical policy and operational gaps, offering scalable solutions for regions facing water scarcity and carbon constraints. The proposed models empower utilities and policymakers to navigate complexity, prioritize investments, and replace disjointed planning practices by embedding risk-aware decision-making, advanced stochastic optimisation, and real-world logistical considerations. This research charts a pathway toward net-zero power systems that are sustainable, resilient, and equitable, ensuring energy security and environmental stewardship amid escalating climate challenges. The holistic frameworks developed here demonstrate that systemic integration, not incremental improvement, is key to decarbonization. This work redefines how energy, water, and carbon systems interact by harmonising flexibility providers and creating adaptive networks capable of addressing global sustainability goals. These innovations provide a blueprint for global energy transitions, showcasing the transformative potential of coordinated optimization in achieving a decarbonized future.

Date of Award	8 May 2025
Original language	American English
Supervisor	AMEENA ALSUMAITI (Supervisor)